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Spatial Statistics

  • Chapter
Landscape Ecology in Theory and Practice

Abstract

In contrast to land-cover or habitat classes, some kinds of landscape data are recorded as continuous numbers rather than discrete categories. Such data include vegetation density or height; net aboveground primary production; nutrient mineralization rates; percent of biomass killed by a disturbance; and distances from lakes, roads, or other features of interest. Most landscape metrics covered in Chap. 4 are not appropriate for quantifying the spatial pattern of continuous variables, and a different set of methods is required. Spatial statistics, including, are used to quantify the spatial structure of continuous data, and they are widely applied in landscape ecology. Spatial statistics and geostatistics use point data for some property that is spatially distributed across the landscape; they do not require categorization of the landscape nor do they assume a patchy structure or the presence of boundaries. Observations, conventionally labeled as z, are made at specific x, y locations and referred to as (Palmer 1992). Spatial statistics then quantify spatial dependence in the regionalized variable, or the tendency of z measured at one x, y location to be correlated with, or depend on, values of z measured at another x, y location. If there is spatial dependence in z, then information about z at one place allows you to infer information about z at another place. Spatial statistics quantify the magnitude of variance in the data, the proportion of that variance that is spatially dependent (i.e., spatially autocorrelated), and the scales, or distances, over which variables are spatially dependent. These methods are powerful, but the terminology and methods can be daunting for those new to the subject.

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Author information

Authors and Affiliations

  1. Department of Zoology, University of Wisconsin-Madison, Madison, WI, USA

    Monica G. Turner

  2. Center for Environmental Science, University of Maryland, Frostburg, MD, USA

    Robert H. Gardner

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  1. Monica G. Turner
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  2. Robert H. Gardner
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Further Reading

Further Reading

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  • Rossi RE, Mulla DJ, Journel AG, Franz EH (1992) Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol Monogr 62:277–314

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Turner, M.G., Gardner, R.H. (2015). Spatial Statistics. In: Landscape Ecology in Theory and Practice. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2794-4_5

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